Diagnostic Yield, Outcomes, and Resource Utilization With Different Ambulatory Electrocardiographic Monitoring Strategies

Real-Time Ambulatory ECG Does Not Expedite Care

BACKGROUND

Ambulatory ECG (AECG) monitoring is pivotal to the diagnosis of arrhythmias and can be performed with near "real-time" notification of abnormalities. There are limited data on the relative benefit of real-time monitoring compared with traditional Holter monitoring.

METHODS AND RESULTS

This is a retrospective observational analysis of University of Utah Health patients who underwent ambulatory ECG studies from 2010 to 2022. The study cohort was stratified by patients with an ambulatory ECG that provides real-time event notification (non-Holter) versus those who do not (Holter). The outcomes were cardiac implantable electronic device procedure, ablation procedure, emergency department/hospitalization visit, and initiation of anticoagulation out to 6 months. We identified 20 259 patients, 16 650 with non-Holter studies and 3609 with Holter studies. Holter patients were younger (mean 52 versus 55, P<0.001), more often women (60.2% versus 57%, P<0.001), and had lower mean CHADS2-VA2Sc scores (1.7 versus 2.1, P<0.001). The median time to ablation procedure was 74 versus 72 (P=0.5), for Holter versus non-Holter, respectively. Median days to new cardiac implantable electronic device implantation was 54 days versus 52 (P=0.6); initiation of anticoagulation among patients not already treated was 42 versus 31 days (P=0.03). Time to first emergency department visit or hospitalization was 63 versus 57 (P=0.6). In multivariable models, there were no significant differences in time to intervention between Holter and non-Holter for each outcome.

CONCLUSIONS

Real-time monitoring demonstrates mixed results in terms of reducing time to intervention, with the significant benefit limited to oral anticoagulation initiation. It is time to revisit clinical scenarios where real-time ambulatory monitoring may not improve health care efficiency.

Extended Ambulatory ECG Monitoring Enhances Identification of Higher-Risk Ventricular Tachyarrhythmias in Patients With Hypertrophic Cardiomyopathy

BACKGROUND

In hypertrophic cardiomyopathy (HCM), 48-hour ambulatory monitoring has been standard practice to detect nonsustained ventricular tachycardia (NSVT), a sudden death risk marker. Extended-wear ambulatory electrocardiographic (ECG) devices have more recently used for monitoring patients with HCM.

OBJECTIVE

We aimed to evaluate NSVT burden identified with continuous ambulatory monitoring for up to 2 weeks compared with initial 48 hours.

METHODS

236 consecutive patients with HCM (49 ± 12 years) underwent 14-day continuous ambulatory monitoring (Zio XT, iRhythm Technologies, San Francisco, CA); diagnostic yield of NSVT compared for initial 48 hours vs extended for 14 days.

RESULTS

Of the 236 patients, 114 (48%) had ≥ 1 runs of NSVT (median 2) over 14 days. Median length of NSVT was 7 beats (range: 3 to 67) at rates of 120 to 240 beats per minute (bpm) (median, 167 bpm). In 42 of the 114 patients (37%), initial NSVT occurred ≤ 48 hours and in 72 (63%) only during the extended monitoring period (3 to 14 days). Diagnostic yield for detecting NSVT over 14 days was 2.7-fold greater than ≤ 48 hours (P < .001). NSVT judged at higher risk (≥ 8 beats, > 200 bpm, ≥ 2 runs in consecutive 2-day period) was identified more frequently during extended monitoring, diagnostic yield over 14 days was 3-fold greater than ≤ 48 hours (P < .001).

CONCLUSION

In HCM, NSVT episodes are frequent; however, in most patients, both NSVT and higher-risk NSVT were not detected during initial 48 hours and were confined solely to extended monitoring period. These data support additional clinical studies to evaluate the significance of NSVT on extended monitoring on sudden death risk in HCM.

Comparative effectiveness and healthcare utilization for ambulatory cardiac monitoring strategies in Medicare beneficiaries

BACKGROUND

Objective data comparing the diagnostic performance of different ambulatory cardiac monitors (ACMs) are lacking.

OBJECTIVES

To assess variation in monitoring strategy, clinical outcomes and healthcare utilization in patients undergoing ambulatory monitoring without a pre-existing arrhythmia diagnosis.

METHODS

Using the full sample (100%) of Medicare claims data, we performed a retrospective cohort study of diagnostic-naïve patients who received first-time ACM in 2017 to 2018 and evaluated arrhythmia encounter diagnosis at 3-months, repeat ACM testing at 6 months, all-cause 90-day emergency department (ED) and inpatient utilization, and cost of different strategies: Holter; long-term continuous monitor (LTCM); non-continuous, event-based external ambulatory event monitor (AEM); and mobile cardiac telemetry (MCT). We secondarily performed a device-specific analysis by manufacturer, identified from unique claim modifier codes.

RESULTS

ACMs were used in 287,789 patients (AEM = 10.3%; Holter = 53.8%; LTCM = 13.3%; MCT = 22.5%). Device-specific analysis showed that compared to Holter, AEM, MCT, or other LTCM manufacturers, a specific LTCM (ZioⓇ XT 14-day patch, iRhythm Technologies, San Francisco, CA) had the highest adjusted odds of diagnosis and lowest adjusted odds of ACM retesting. Findings were consistent for specific arrhythmia diagnoses of ventricular tachycardia, atrioventricular block, and paroxysmal atrial fibrillation. As a category, LTCM was associated with the lowest 1-year incremental health care expenditures (mean Δ$10,159), followed by Holter ($10,755), AEM ($11,462), and MCT ($12,532).

CONCLUSIONS

There was large variation in diagnostic monitoring strategy. A specific LTCM was associated with the highest adjusted odds of a new arrhythmia diagnosis and lowest adjusted odds of repeat ACM testing. LTCM as a category had the lowest incremental acute care utilization. Different monitoring strategies may produce different results with respect to diagnosis and care.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.